DDA: a traceable multi-agent framework for automated structure-based drug design

Abstract

Structure-Based Drug Design (SBDD) is a key computational paradigm that uses protein structural information to design and optimize small molecules with desired binding properties. Existing SBDD methods mainly focus on molecular design and often lack the capability to independently conduct downstream evaluation and optimize workflows. Meanwhile, directly applying large language models (LLMs) to molecular design faces challenges such as fragmented execution, limited tool coordination, and insufficient traceability of intermediate decision-making processes. To address these limitations, we propose a Drug Discovery Agent (DDA), a traceable and auditable multi-agent biomedical informatics framework for automating SBDD. DDA uses a role-specific multi-agent architecture to transform natural-language drug-discovery goals into actionable scientific workflows. Through a unified tool-calling protocol, this framework seamlessly integrates bioinformatics, molecular modeling, and molecular docking modules, enabling autonomous workflow execution from target preparation and molecular generation to multi-objective evaluation, candidate prioritization, and trajectory tracking. We systematically evaluated DDA on the CrossDocked2020 benchmark. Under the closed-loop delivery protocol, the framework produced 2,000 final candidate records, with a joint screen-pass rate 20 percentage points higher than that of the strongest specialized baseline. These results indicate that DDA provides a scalable, executable, and traceable computational framework that reduces manual coordination in structure-based automated drug discovery and delivers prioritized candidate sets with minimal human intervention.